A network equipment management system and method based on Internet of Things

Abstract

The present invention discloses a network device management system and method based on the Internet of Things, belonging to the technical field of the Internet of Things. The present invention traverses the underlying network architecture of a regional network, obtains basic attribute data and real-time status data of each network device, and generates an underlying device information form; divides each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and derives the corresponding sub-network device information form; performs periodic data flow self-learning on each network device according to the sub-network device information form, performs data flow complexity detection on the network device according to the result, and performs dynamic network resource management on each network device in combination with the analysis result; sets up a database node in the corresponding network according to the corresponding sub-network device information form, performs regional network device data storage, and dynamically monitors network resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a network equipment management system and method based on Internet of Things. Background Art

[0002] Network device management refers to the process of monitoring, configuring, maintaining, and troubleshooting various hardware devices in the network; these devices usually include routers, switches, firewalls, servers, workstations, and other network-connected devices; the purpose of network device management is to ensure the stability, security, and efficiency of the network while maximizing the utilization of network resources;

[0003] Under the existing network environment, due to the development of the Internet and the needs of various fields of society, the network coverage is wide and deep; the number of network devices connected to the network is increasing exponentially in various regions, thus causing a series of network problems; the more typical one is that the increase in the number of devices brings a large amount of data traffic and file data to the network, and the existence of a large amount of repeated or highly overlapping data content will cause waste and congestion of network resources; secondly, the access of a large number of network devices to the network will make it difficult to allocate network resources, which can easily lead to unreasonable allocation of network resources. Summary of the invention

[0004] The purpose of the present invention is to provide a network device management system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A network device management method based on the Internet of Things, the method comprising the following steps:

[0007] S100, traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form;

[0008] S200, dividing each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and deriving a corresponding sub-network device information form from the underlying device information form;

[0009] S300, performing periodic data flow self-learning analysis on each network device according to the sub-networking device information form, performing data flow complexity detection analysis on the network device according to the analysis result, and performing dynamic network resource management on each network device in combination with the periodic data flow self-learning analysis result and the data flow complexity detection analysis result of each network device;

[0010] S400: Establish a database node in the corresponding network according to the corresponding sub-network device information form, store regional network device data, and dynamically monitor network resource management.

[0011] The specific steps of S100 for traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form are as follows:

[0012] S101, using the regional local area network to continuously traverse the entire path of the network access points of the underlying network devices, assigning temporary spatial numbers to the traversed devices until the entire traversal of the underlying network is completed; and retrieving the traversed devices in the underlying network by number;

[0013] S102. In the corresponding background local area network device database, the relative spatial position of each device is restored according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and the underlying data information of each device is retrieved; the underlying data information includes device type information, brand model information, network protocol information and IP address information; in combination with the retrieved information, an integrated data strip is generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate a corresponding underlying device information form; wherein the device type information and brand model information are the basic attribute data of the device; the network protocol information and IP address information are the real-time status data of the device; the data strips are merged and spliced ​​to generate the corresponding underlying device information form by determining any data strip as the form header data strip, and splicing the relative spatial positions of the remaining data strips corresponding to each device.

[0014] The specific steps of S200 in which each network device is divided into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and the corresponding sub-network device information form is derived from the underlying device information form are as follows:

[0015] S201, retrieve the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locate the device node in the local area network through the retrieved device IP address data; obtain the corresponding subnet mask according to the device IP address, and determine the subnet area where the device is located;

[0016] S202, determine the boundary of the subnet area where the device is located, and mark each device in the corresponding boundary, and perform secondary networking division in the subnet boundary based on the type data of each device; divide the devices with the same type of data into the same networking set, and construct the networking set as

[0017] in is the device in the corresponding networking set after division; α is the type data label of the device; n corresponds to the number of devices in the same type of networking set; and the sub-networking device information form is derived for each device according to the divided networking set under each sub-network domain.

[0018] The specific steps of performing periodic data flow self-learning analysis on each network device according to the sub-networking device information form, performing data flow complexity detection analysis on the network device according to the analysis results, and performing dynamic network resource management on each network device in combination with the periodic data flow self-learning analysis results and the data flow complexity detection analysis results of each network device are as follows:

[0019] S301. Retrieve the periodic data stream of each device according to the sub-network device information form; wherein the periodic data stream is the data packet sent or received by the device within the set period; if the running time of a device is less than the period time, implement virtual self-learning operation on the device, by taking the data stream record within the running time of the current device as sample data, randomly select the sample data packet data within each time point to form a sample data stream set, and summarize the formed set to construct a virtual sample data stream set group, and calculate the analogy between each set data stream in the virtual sample data stream set group and the data stream set within the running time of the actual device, and the calculation formula is:

[0020]

[0021] Among them, An(N i :NS) is the analogy between each sample data stream set in the virtual sample data stream set group and the actual device data stream set; L(N i :NS) is the length of the element sequence in each sample data stream set that overlaps with the actual device data stream set; L all N is the actual device data stream set length; i is each sample data stream set in the virtual sample data stream set group; NS is the data stream set during the running time of the actual device; i is the label of the corresponding sample data stream set in the virtual sample data stream set group; each sample data stream set has the same length as the actual device data stream set; for the analogy calculation result, select MIN(An(N i:Ns)) corresponds to a self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analysis is performed on the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, the above virtual self-learning operation is repeated until the set period length is met; wherein the data stream set splicing is to splice the sample data stream set corresponding to the analogy selection result to the end of the actual device data stream set; the data stream set time is the sum of the corresponding time length of the sample data stream set selected for splicing and the actual running time length of the current device;

[0022] S302, perform data flow complexity detection and analysis on the periodic data retrieved by each device; continuously calculate the data packets in the period in the order of their corresponding time lines, and the calculation formula is: Where An(m-1,m) is the analogy of the data packets between adjacent time points in the corresponding device cycle data; Z(m-1) and Z(m) are the data segment sets constructed by cutting the data packet content at each time point in the device cycle data with a sequence length of t; MAX[Z(m-1),Z(m)] is the larger of the set lengths Z(m-1) and Z(m); the corresponding data packet analogy preset value An is given by the system β ; If An(m-1,m)>An β , then the data packets at the adjacent time points are judged to be high overlap data packets; if An(m-1,m)≤An β , then the data packets at the adjacent time points are judged to be heterogeneous data packets; the analogy degree of the data packets at adjacent time points of the periodic data of each device is fully calculated, and the number of heterogeneous data packets is counted for the calculation results, and the data flow complexity of the periodic data is calculated based on the statistical results. The calculation formula is: Where Com is the data flow complexity of the periodic data of each device; U[An(m-1,m)≤An β ] is the number of heterogeneous data packets in the corresponding device cycle data; U all is the total number of data packets in the corresponding device cycle data; the midpoint of the number of devices in the sub-network device information form is taken as the proposed mapping threshold position, the calculation results of the data flow complexity of each device cycle data are sorted from large to small, and the data flow complexity corresponding to the proposed mapping threshold position is taken as the threshold Com β , then for Com <Com β The data packet content of the device is periodically planned, the data packet is segmented and the repeated fragment content in the high overlap data packet is taken as the fixed data packet format specification, and the non-repeated part is taken as the dynamic format specification; for Com≥Com βThe data packets of the devices are not intervened; if the number of devices is an odd number, the midpoint is taken; if it is an even number, the complexity of the two adjacent sides of the midpoint is taken for average calculation, and the calculation result is used as the threshold; for Com <Com β The device performs data packet intervention processing, specifically, after the data packet content is segmented, the highly overlapping fragments are used as the fixed composition form of the highly overlapping data packet, and are constructed at the target endpoint, while the device only needs to transmit the non-overlapping fragments. After the target endpoint receives the non-overlapping fragment content, it fills the empty packets according to the fixed data packet composition form to complete the composition of the complete content of the data packet; this method can avoid highly overlapping data packets occupying a large amount of network resources, causing resource waste;

[0023] S303, combining the above analysis results to dynamically manage network resources for each network device; by analyzing the network resource usage of each network device within the cycle, the calculation formula is: Where BUC is the network bandwidth conversion rate of each device in the cycle; T is the cycle duration; p is the number of data packets transmitted by the device at each time point in the cycle; B p The bandwidth occupied by the data packets transmitted at each time point in the corresponding cycle; the system makes an internal comparison based on the bandwidth occupancy conversion rate of each device in the cycle, and the comparison formula is BUC1:BUC2:...:BUC n , dynamically allocate the bandwidth required by each device within the cycle according to the proportion result; since some devices will repeatedly send high-overlapping data or have idle time within the cycle, which will cause network resources to be idle or wasted, the network resources of each device are dynamically reallocated based on the complexity of the data flow sent by the device within the cycle and the bandwidth occupancy at each time point to make reasonable use of network resources.

[0024] The specific steps of S400 for setting up a database node in the corresponding network according to the corresponding sub-network device information form, storing regional network device data, and dynamically monitoring network resource management are as follows:

[0025] S401, marking device nodes in the corresponding local area network according to the sub-network device information form, and extending through the center point connection line to obtain the associated path point of each device in the space, and use this point as the database construction point;

[0026] S402: Record the entire analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generate a comprehensive report based on the analysis results and allocation results and feed it back to the local area network management backend.

[0027] A network equipment management system based on the Internet of Things, the system comprising an equipment data acquisition module, a networking division module, a network equipment comprehensive analysis module and a data report feedback module;

[0028] The device data acquisition module traverses the underlying network architecture of the network devices in the regional network, acquires the basic attribute data and real-time status data of each network device, and generates an underlying device information form; the networking division module divides each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and derives the corresponding sub-network device information form from the underlying device information form; the network device comprehensive analysis module performs periodic data flow self-learning analysis on each network device according to the sub-network device information form, performs data flow complexity detection analysis on the network devices according to the analysis results, and dynamically manages network resources for each network device in combination with the periodic data flow self-learning analysis results and the data flow complexity detection analysis results of each network device; the data report feedback module sets up database nodes in the corresponding network according to the corresponding sub-network device information form, stores regional network device data, and dynamically monitors network resource management.

[0029] The device data acquisition module includes a bottom-level network traversal unit and a bottom-level device information form generation unit; the bottom-level network traversal unit uses the regional local area network to continuously traverse the entire path of the bottom-level network device network access point, and assigns spatial temporary numbers to the traversed devices until the bottom-level network is fully traversed; the traversed devices in the bottom-level network are numbered and retrieved; the bottom-level device information form generation unit restores the relative spatial position of the devices in the corresponding background local area network device database according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and retrieves the bottom-level data information of each device; the bottom-level data information includes device type information, brand model information, network protocol information and IP address information; combined with the retrieved information, integrated data strips are generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate the corresponding bottom-level device information form.

[0030] The network division module includes a subnet area determination unit and a subnet device information form generation unit; the subnet area determination unit retrieves the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locates the device node in the local area network through the retrieved IP address data of the device; obtains the corresponding subnet mask according to the IP address of the device, and determines the subnet area where the device is located; the subnet device information form generation unit determines the boundary of the subnet area where the device is located, and marks each device in the corresponding boundary, and performs secondary network division in the subnet boundary based on the type data of each device; divides devices with the same type of data into the same networking set; and derives the subnet device information form for each device according to the divided networking set under each subnet boundary.

[0031] The network equipment comprehensive analysis module includes a device virtual self-learning unit, a device periodic data flow complexity analysis unit and a dynamic network resource allocation unit; the device virtual self-learning unit retrieves the periodic data flow of each device according to the sub-networking device information form; the periodic data flow is a data packet sent or received by the device within a set period; if the device operation time is less than the period time, a virtual self-learning operation is performed on the device, and the data flow record within the operation time of the current device is used as the sample data, and the sample data packet data within each time point is randomly selected to form a sample data flow set, and the composed set is summed up to construct a virtual sample data flow set group, and the virtual sample data flow is The analogy between each set data stream in the set group and the data stream set within the operating time of the actual device is calculated; the sample data stream set corresponding to the minimum value of the analogy calculation result is taken as the self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analysis is performed on the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, the above-mentioned virtual self-learning operation is repeated until the set period length is met; the device period data stream complexity analysis unit performs data stream complexity analysis on the period data retrieved by each device Degree detection and analysis; continuously calculate the data packets in the cycle in the order of their corresponding timeline; the system gives the corresponding data packet analogy preset value, compares the calculation result with the preset value, and classifies the data packets at adjacent time points in the result cycle, whose types include highly overlapping data packets and heterogeneous data packets; fully calculate the analogy of the data packets at adjacent time points of the cycle data of each device, and count the number of heterogeneous data packets for the calculation results, and calculate the data flow complexity of the cycle data based on the statistical results; take the midpoint of the number of devices in the sub-network device information form as the proposed mapping threshold position, sort the calculation results of the data flow complexity of the cycle data of each device from large to small, and take The data flow complexity corresponding to the proposed mapping threshold position is the threshold, the calculation result is compared with the threshold, the device data packet content that is less than the threshold is periodically planned, the data packet is segmented and the repeated fragment content in the high overlap data packet is taken as the fixed data packet form specification, and the non-repeated part is taken as the dynamic form specification, otherwise no intervention processing is performed; the dynamic network resource allocation unit performs dynamic network resource management on each network device in combination with the above analysis results; analyzes and calculates the network resource usage of each network device within the cycle; the system performs internal comparison based on the bandwidth occupancy conversion rate of each device within the cycle; and dynamically allocates the bandwidth required by each device within the cycle according to the proportional result.

[0032] The data report feedback module includes a database construction unit and a comprehensive report feedback unit; the database construction unit marks the device nodes in the corresponding local area network according to the sub-networking device information form, and extends through the center point connection line to obtain the associated path point of each device in the space, and uses this point as the database construction point; the comprehensive report feedback unit records the full analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generates a comprehensive report based on the analysis results and allocation results and feeds it back to the local area network management backend.

[0033] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention utilizes multiple modules and multiple units to realize dynamic network management of network device nodes in a local area network; wherein the information acquisition of device nodes is realized by traversing the underlying network architecture, and the local area network device nodes are divided into fine blocks through internal subnets for networking, and partial data packet form planning and data flow complexity calculation are performed by analyzing the data flow content structure of each device node, thereby reducing the waste of network resources; the bandwidth occupancy conversion of the device node period is analyzed to realize dynamic allocation of network resources; the present invention can realize streamlined planning of data flows in the network and rationalization of network resources by performing multi-level division in the local area network and deeply analyzing the data flow status of each device period, thereby reducing the waste of network resources and imbalanced distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0035] Figure 1 It is a structural schematic diagram of a network device management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 , the present invention provides a technical solution:

[0038] A network device management method based on the Internet of Things, the method comprising the following steps:

[0039] S100, traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form;

[0040] S200, dividing each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and deriving a corresponding sub-network device information form from the underlying device information form;

[0041] S300, performing periodic data flow self-learning analysis on each network device according to the sub-networking device information form, performing data flow complexity detection analysis on the network device according to the analysis result, and performing dynamic network resource management on each network device in combination with the periodic data flow self-learning analysis result and the data flow complexity detection analysis result of each network device;

[0042] S400: Establish a database node in the corresponding network according to the corresponding sub-network device information form, store regional network device data, and dynamically monitor network resource management.

[0043] The specific steps of S100 for traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form are as follows:

[0044] S101, using the regional local area network to continuously traverse the entire path of the network access points of the underlying network devices, assigning temporary spatial numbers to the traversed devices until the entire traversal of the underlying network is completed; and retrieving the traversed devices in the underlying network by numbering;

[0045] S102. In the corresponding background local area network device database, the relative spatial position of each device is restored according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and the underlying data information of each device is retrieved; the underlying data information includes device type information, brand model information, network protocol information and IP address information; combined with the retrieved information, integrated data strips are generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate the corresponding underlying device information form.

[0046] The specific steps of S200 in which each network device is divided into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and the corresponding sub-network device information form is derived from the underlying device information form are as follows:

[0047] S201, retrieve the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locate the device node in the local area network through the retrieved device IP address data; obtain the corresponding subnet mask according to the device IP address, and determine the subnet area where the device is located;

[0048] S202, determine the boundary of the subnet area where the device is located, and mark each device in the corresponding boundary, and perform secondary networking division in the subnet boundary based on the type data of each device; divide the devices with the same type of data into the same networking set, and construct the networking set as

[0049] in is the device in the corresponding networking set after division; α is the type data label of the device; n corresponds to the number of devices in the same type of networking set; and the sub-networking device information form is derived for each device according to the divided networking set under each sub-network domain.

[0050] The specific steps of performing periodic data flow self-learning analysis on each network device according to the sub-networking device information form, performing data flow complexity detection analysis on the network device according to the analysis results, and performing dynamic network resource management on each network device in combination with the periodic data flow self-learning analysis results and the data flow complexity detection analysis results of each network device are as follows:

[0051] S301. Retrieve the periodic data stream of each device according to the sub-network device information form; wherein the periodic data stream is the data packet sent or received by the device within the set period; if the running time of a device is less than the period time, implement virtual self-learning operation on the device, by taking the data stream record within the running time of the current device as sample data, randomly select the sample data packet data within each time point to form a sample data stream set, and summarize the formed set to construct a virtual sample data stream set group, and calculate the analogy between each set data stream in the virtual sample data stream set group and the data stream set within the running time of the actual device, and the calculation formula is:

[0052]

[0053] Among them, An(N i :NS) is the analogy between each sample data stream set in the virtual sample data stream set group and the actual device data stream set; L(N i :NS) is the length of the element sequence in each sample data stream set that overlaps with the actual device data stream set; L all N is the actual device data stream set length; iis each sample data stream set in the virtual sample data stream set group; NS is the data stream set during the running time of the actual device; i is the label of the corresponding sample data stream set in the virtual sample data stream set group; according to the analogy calculation result, select MIN(An(N i :Ns)) corresponds to the sample data stream set as the self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analysis is performed on the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, the above virtual self-learning operation is repeated until the set period length is met;

[0054] S302, perform data flow complexity detection and analysis on the periodic data retrieved by each device; continuously calculate the data packets in the period in the order of their corresponding time lines, and the calculation formula is: Where An(m-1,m) is the analogy of the data packets between adjacent time points in the corresponding device cycle data; Z(m-1) and Z(m) are the data segment sets constructed by cutting the data packet content at each time point in the device cycle data with a sequence length of t; MAX[Z(m-1),Z(m)] is the larger of the set lengths Z(m-1) and Z(m); the corresponding data packet analogy preset value An is given by the system β ; If An(m-1,m)>An β , then the data packets at the adjacent time points are judged to be high overlap data packets; if An(m-1,m)≤An β , then the data packets at the adjacent time points are judged to be heterogeneous data packets; the analogy degree of the data packets at adjacent time points of the periodic data of each device is fully calculated, and the number of heterogeneous data packets is counted for the calculation results, and the data flow complexity of the periodic data is calculated based on the statistical results. The calculation formula is: Where Com is the data flow complexity of the periodic data of each device; U[An(m-1,m)≤An β ] is the number of heterogeneous data packets in the corresponding device cycle data; U all is the total number of data packets in the corresponding device cycle data; the midpoint of the number of devices in the sub-network device information form is taken as the proposed mapping threshold position, the calculation results of the data flow complexity of each device cycle data are sorted from large to small, and the data flow complexity corresponding to the proposed mapping threshold position is taken as the threshold Com β , then for Com <Com β The data packet content of the device is periodically planned, the data packet is segmented and the repeated fragment content in the high overlap data packet is taken as the fixed data packet format specification, and the non-repeated part is taken as the dynamic format specification; for Com≥Comβ The data packets of the device are not intervened;

[0055] S303, combining the above analysis results to dynamically manage network resources for each network device; by analyzing the network resource usage of each network device within the cycle, the calculation formula is:

[0056] Where BUC is the network bandwidth conversion rate of each device in the cycle; T is the cycle duration; p is the number of data packets transmitted by the device at each time point in the cycle; B p The bandwidth occupied by the data packets transmitted at each time point in the corresponding cycle; the system makes an internal comparison based on the bandwidth occupancy conversion rate of each device in the cycle, and the comparison formula is BUC1:BUC2:...:BUC n , dynamically allocate the bandwidth required by each device within the cycle based on the ratio result.

[0057] The specific steps of S400 for setting up a database node in the corresponding network according to the corresponding sub-network device information form, storing regional network device data, and dynamically monitoring network resource management are as follows:

[0058] S401, marking device nodes in the corresponding local area network according to the sub-network device information form, and extending through the center point connection line to obtain the associated path point of each device in the space, and use this point as the database construction point;

[0059] S402: Record the entire analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generate a comprehensive report based on the analysis results and allocation results and feed it back to the local area network management backend.

[0060] A network equipment management system based on the Internet of Things, the system comprising an equipment data acquisition module, a networking division module, a network equipment comprehensive analysis module and a data report feedback module;

[0061] The device data acquisition module traverses the underlying network architecture of the network devices in the regional network, acquires the basic attribute data and real-time status data of each network device, and generates an underlying device information form; the networking division module divides each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and derives the corresponding sub-network device information form from the underlying device information form; the network device comprehensive analysis module performs periodic data flow self-learning analysis on each network device according to the sub-network device information form, performs data flow complexity detection analysis on the network devices according to the analysis results, and dynamically manages network resources for each network device in combination with the periodic data flow self-learning analysis results and the data flow complexity detection analysis results of each network device; the data report feedback module sets up database nodes in the corresponding network according to the corresponding sub-network device information form, stores regional network device data, and dynamically monitors network resource management.

[0062] The device data acquisition module includes a bottom-level network traversal unit and a bottom-level device information form generation unit; the bottom-level network traversal unit uses the regional local area network to continuously traverse the entire path of the bottom-level network device network access point, and assigns spatial temporary numbers to the traversed devices until the bottom-level network is fully traversed; the traversed devices in the bottom-level network are numbered and retrieved; the bottom-level device information form generation unit restores the relative spatial position of the devices in the corresponding background local area network device database according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and retrieves the bottom-level data information of each device; the bottom-level data information includes device type information, brand model information, network protocol information and IP address information; combined with the retrieved information, integrated data strips are generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate the corresponding bottom-level device information form.

[0063] The network division module includes a subnet area determination unit and a subnet device information form generation unit; the subnet area determination unit retrieves the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locates the device node in the local area network through the retrieved IP address data of the device; obtains the corresponding subnet mask according to the IP address of the device, and determines the subnet area where the device is located; the subnet device information form generation unit determines the boundary of the subnet area where the device is located, and marks each device in the corresponding boundary, and performs secondary network division in the subnet boundary based on the type data of each device; divides devices with the same type of data into the same networking set; and derives the subnet device information form for each device according to the divided networking set under each subnet boundary.

[0064] The network equipment comprehensive analysis module includes a device virtual self-learning unit, a device periodic data flow complexity analysis unit and a dynamic network resource allocation unit; the device virtual self-learning unit retrieves the periodic data flow of each device according to the sub-networking device information form; the periodic data flow is a data packet sent or received by the device within a set period; if the device operation time is less than the period time, a virtual self-learning operation is performed on the device, and the data flow record within the operation time of the current device is used as the sample data, and the sample data packet data within each time point is randomly selected to form a sample data flow set, and the composed set is summed up to construct a virtual sample data flow set group, and the virtual sample data flow is The analogy between each set data stream in the set group and the data stream set within the operating time of the actual device is calculated; the sample data stream set corresponding to the minimum value of the analogy calculation result is taken as the self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analysis is performed on the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, the above-mentioned virtual self-learning operation is repeated until the set period length is met; the device period data stream complexity analysis unit performs data stream complexity analysis on the period data retrieved by each device Degree detection and analysis; continuously calculate the data packets in the cycle in the order of their corresponding timeline; the system gives the corresponding data packet analogy preset value, compares the calculation result with the preset value, and classifies the data packets at adjacent time points in the result cycle, whose types include highly overlapping data packets and heterogeneous data packets; fully calculate the analogy of the data packets at adjacent time points of the cycle data of each device, and count the number of heterogeneous data packets for the calculation results, and calculate the data flow complexity of the cycle data based on the statistical results; take the midpoint of the number of devices in the sub-network device information form as the proposed mapping threshold position, sort the calculation results of the data flow complexity of the cycle data of each device from large to small, and take The data flow complexity corresponding to the proposed mapping threshold position is the threshold, the calculation result is compared with the threshold, the device data packet content that is less than the threshold is periodically planned, the data packet is segmented and the repeated fragment content in the high overlap data packet is taken as the fixed data packet form specification, and the non-repeated part is taken as the dynamic form specification, otherwise no intervention processing is performed; the dynamic network resource allocation unit performs dynamic network resource management on each network device in combination with the above analysis results; analyzes and calculates the network resource usage of each network device within the cycle; the system performs internal comparison based on the bandwidth occupancy conversion rate of each device within the cycle; and dynamically allocates the bandwidth required by each device within the cycle according to the proportional result.

[0065] The data report feedback module includes a database construction unit and a comprehensive report feedback unit; the database construction unit marks the device nodes in the corresponding local area network according to the sub-networking device information form, and extends through the center point connection line to obtain the associated path point of each device in the space, and uses this point as the database construction point; the comprehensive report feedback unit records the full analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generates a comprehensive report based on the analysis results and allocation results and feeds it back to the local area network management backend.

[0066] In the example:

[0067] There is a local area network background that uses the regional local area network to continuously traverse the entire path of the network access points of the underlying network devices, and assigns temporary spatial numbers to the traversed devices until the underlying network is fully traversed; the traversed devices in the underlying network are numbered and retrieved; in the corresponding background local area network device database, the relative spatial position of the device is restored according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and the underlying data information of each device is retrieved; combined with the retrieved information, the integrated data strips of each network device are generated, and the data strips corresponding to each device in the local area network are merged Generate corresponding underlying device information forms through splicing; retrieve corresponding IP addresses and device type data of each device in the local area network through the underlying device information forms; locate device nodes in the local area network through the retrieved device IP address data; obtain corresponding subnet mask according to the device IP address, and determine the subnet area where the device is located; determine the boundary of the subnet area where the device is located, and mark each device in the corresponding boundary, and perform secondary networking division in the subnet boundary based on the type data of each device; divide devices with the same type of data into the same networking set, and the networking set is constructed as According to the network set divided under each subnet domain, the subnet device information table is derived for each device;

[0068] According to the sub-network device information form, the periodic data stream of each device is retrieved; wherein the periodic data stream is the data packet sent or received by the device within the set period; if there is a device whose running time is less than the period time, a virtual self-learning operation is performed on the device, by taking the data stream record within the running time of the current device as the sample data, randomly selecting the sample data packet data within each time point to form a sample data stream set, and summing up the composed set to construct a virtual sample data stream set group, and calculating the analogy between each set data stream in the virtual sample data stream set group and the data stream set within the running time of the actual device, the calculation formula is:

[0069]

[0070] For the analogy calculation results, select MIN(An(N i :Ns)) is a self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set of a device in the network is greater than the set period, further analysis is performed on the retrieved periodic data of each device;

[0071] Perform data flow complexity detection and analysis on the periodic data retrieved by each device; continuously calculate the data packets within the period in the order of their corresponding time lines, and the calculation formula is: The system gives the corresponding data packet analogy preset value An β ; If An(m-1,m)>An β , then the data packets at the adjacent time points are judged to be high overlap data packets; if An(m-1,m)≤An β , then the data packets at the adjacent time points are judged to be heterogeneous data packets; the analogy degree of the data packets at adjacent time points of the periodic data of each device is fully calculated, and the number of heterogeneous data packets is counted for the calculation results, and the data flow complexity of the periodic data is calculated based on the statistical results. The calculation formula is: The midpoint of the number of devices in the sub-network device information form is taken as the proposed mapping threshold position, the calculation results of the data flow complexity of each device cycle data are sorted from large to small, and the data flow complexity corresponding to the proposed mapping threshold position is taken as the threshold Com β ; If the amount of device data in the table is 5, the midpoint position is taken as the mapping threshold position, and the corresponding complexity calculation results are sorted in descending order, and the complexity calculation result corresponding to the midpoint is taken as the threshold; if the current device complexity calculation result Com≥Com β , then the data packets of the device will not be interfered with;

[0072] Combined with the above analysis results, dynamic network resource management is performed on each network device; the network resource usage of each network device within the cycle is analyzed and calculated, and the calculation formula is: The system makes an internal comparison based on the bandwidth conversion rate of each device within the period. The comparison formula is BUC1:BUC2:...:BUC n, dynamically allocate the bandwidth required by each device within the cycle according to the proportion results; the system marks the device nodes in the corresponding local area network according to the sub-networking device information form, and extends through the center point connection to obtain the associated path points of each device in space, and uses this point as the database construction point; the entire analysis process and dynamic network resource allocation results of each sub-networking device are recorded through the database, and the analysis results and allocation results are generated into a comprehensive report and fed back to the local area network management backend.

[0073] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0074] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A network device management method based on the Internet of Things, characterized in that: The method comprises the following steps: S100, traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form; S200, dividing each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and deriving a corresponding sub-network device information form from the underlying device information form; S300, performing periodic data flow self-learning analysis on each network device according to the sub-networking device information form, performing data flow complexity detection analysis on the network device according to the analysis result, and performing dynamic network resource management on each network device in combination with the periodic data flow self-learning analysis result and the data flow complexity detection analysis result of each network device; The specific steps of S300 are as follows: S301. Retrieve the periodic data stream of each device according to the sub-network device information form; wherein the periodic data stream is the data packet sent or received by the device within the set period; if the running time of a device is less than the period time, implement virtual self-learning operation on the device, by taking the data stream record within the running time of the current device as sample data, randomly select the sample data packet data within each time point to form a sample data stream set, and summarize the formed set to construct a virtual sample data stream set group, and calculate the analogy between each set data stream in the virtual sample data stream set group and the data stream set within the running time of the actual device, and the calculation formula is: Among them, An(N i :NS) is the analogy between each sample data stream set in the virtual sample data stream set group and the actual device data stream set; L(N i :NS) is the length of the element sequence in each sample data stream set that overlaps with the actual device data stream set; L all N is the actual device data stream set length; i is each sample data stream set in the virtual sample data stream set group; NS is the data stream set during the running time of the actual device; i is the label of the corresponding sample data stream set in the virtual sample data stream set group; according to the analogy calculation result, select MIN(An(N i :Ns)) corresponds to the sample data stream set as the self-learning data stream set, which is spliced ​​with the actual device data stream set, and the time length of the spliced ​​data stream set is compared with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analysis is performed on the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, the above virtual self-learning operation is repeated until the set period length is met; S302, perform data flow complexity detection and analysis on the periodic data retrieved by each device; continuously calculate the data packets in the period in the order of their corresponding time lines, and the calculation formula is: Where An(m-1,m) is the analogy of the data packets between adjacent time points in the corresponding device cycle data; Z(m-1) and Z(m) are the data segment sets constructed by cutting the data packet content at each time point in the device cycle data with a sequence length of t; MAX[Z(m-1),Z(m)] is the larger of the set lengths Z(m-1) and Z(m); the corresponding data packet analogy preset value An is given by the system β ; If An(m-1,m)>An β , then the data packets at the adjacent time points are judged to be high overlap data packets; if An(m-1,m)≤An β , then the data packets at the adjacent time points are judged to be heterogeneous data packets; the analogy degree of the data packets at adjacent time points of the periodic data of each device is fully calculated, and the number of heterogeneous data packets is counted for the calculation results, and the data flow complexity of the periodic data is calculated based on the statistical results. The calculation formula is: Where Com is the data flow complexity of the periodic data of each device; U[An(m-1,m)≤An β ] is the number of heterogeneous data packets in the corresponding device cycle data; U all is the total number of data packets in the corresponding device cycle data; the midpoint of the number of devices in the sub-network device information form is taken as the proposed mapping threshold position, the calculation results of the data flow complexity of each device cycle data are sorted from large to small, and the data flow complexity corresponding to the proposed mapping threshold position is taken as the threshold Com β , then for Com <Com β The data packet content of the device is periodically planned, the data packet is segmented and the repeated fragment content in the high overlap data packet is taken as the fixed data packet format specification, and the non-repeated part is taken as the dynamic format specification; for Com≥Com β The data packets of the device are not intervened; S303, combining the above analysis results to dynamically manage network resources for each network device; by analyzing the network resource usage of each network device within the cycle, the calculation formula is: Where BUC is the network bandwidth conversion rate of each device in the cycle; T is the cycle duration; p is the number of data packets transmitted by the device at each time point in the cycle; B p The bandwidth occupied by the data packets transmitted at each time point in the corresponding cycle; the system makes an internal comparison based on the bandwidth occupancy conversion rate of each device in the cycle, and the comparison formula is BUC1:BUC2:...:BUC n , dynamically allocate the bandwidth required by each device within the period according to the ratio result; S400: Establish a database node in the corresponding network according to the corresponding sub-network device information form, store regional network device data, and dynamically monitor network resource management.

2. According to claim 1, a network device management method based on the Internet of Things is characterized in that: The specific steps of S100 for traversing the underlying network architecture of network devices in the regional network, acquiring basic attribute data and real-time status data of each network device, and generating an underlying device information form are as follows: S101, using the regional local area network to continuously traverse the entire path of the network access points of the underlying network devices, assigning temporary spatial numbers to the traversed devices until the entire traversal of the underlying network is completed; and retrieving the traversed devices in the underlying network by number; S102. In the corresponding background local area network device database, the relative spatial position of each device is restored according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and the underlying data information of each device is retrieved; the underlying data information includes device type information, brand model information, network protocol information and IP address information; combined with the retrieved information, integrated data strips are generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate the corresponding underlying device information form.

3. According to claim 2, a network device management method based on the Internet of Things is characterized in that: The specific steps of S200 for dividing each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and deriving the corresponding sub-network device information form from the underlying device information form are as follows: S201, retrieve the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locate the device node in the local area network through the retrieved device IP address data; obtain the corresponding subnet mask according to the device IP address, and determine the subnet area where the device is located; S202, determine the boundary of the subnet area where the device is located, and mark each device in the corresponding boundary, and perform secondary networking division in the subnet boundary based on the type data of each device; divide the devices with the same type of data into the same networking set, and construct the networking set as in is the device in the corresponding networking set after division; α is the type data label of the device; n corresponds to the number of devices in the same type of networking set; and the sub-networking device information form is derived for each device according to the divided networking set under each sub-network domain.

4. The method for managing network equipment based on the Internet of Things according to claim 3, characterized in that: The specific steps of S400 for setting up a database node in the corresponding network according to the corresponding sub-network device information form, storing regional network device data, and dynamically monitoring network resource management are as follows: S401, marking device nodes in the corresponding local area network according to the sub-network device information form, and extending through the center point connection line to obtain the associated path point of each device in the space, and use this point as the database construction point; S402: Record the entire analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generate a comprehensive report based on the analysis results and allocation results and feed it back to the local area network management backend.

5. A network device management system based on the Internet of Things, applying a network device management method based on the Internet of Things as claimed in any one of claims 1 to 4, characterized in that: The system includes a device data acquisition module, a networking division module, a network device comprehensive analysis module and a data report feedback module; The device data acquisition module traverses the underlying network architecture of the network devices in the regional network, acquires the basic attribute data and real-time status data of each network device, and generates an underlying device information form; the network division module divides each network device into sub-networks according to the underlying device information form in combination with the spatial attributes and type attributes of each network device, and derives the corresponding sub-network device information form by the underlying device information form; The network equipment comprehensive analysis module performs periodic data flow self-learning analysis on each network device according to the sub-networking equipment information form, performs data flow complexity detection and analysis on the network equipment according to the analysis results, and dynamically manages network resources for each network device in combination with the periodic data flow self-learning analysis results and the data flow complexity detection and analysis results of each network device; the data report feedback module sets up database nodes in the corresponding network according to the corresponding sub-networking equipment information form, stores regional network equipment data, and dynamically monitors network resource management.

6. The network device management system based on the Internet of Things according to claim 5, characterized in that: The device data acquisition module includes a bottom-level network traversal unit and a bottom-level device information form generation unit; the bottom-level network traversal unit uses the regional local area network to continuously traverse the entire path of the bottom-level network device network access point, and assigns spatial temporary numbers to the traversed devices until the bottom-level network is fully traversed; the traversed devices in the bottom-level network are numbered and retrieved; the bottom-level device information form generation unit restores the relative spatial position of the devices in the corresponding background local area network device database according to the temporary number of each device to obtain the relative spatial position coordinates of each device, and retrieves the bottom-level data information of each device; the bottom-level data information includes device type information, brand model information, network protocol information and IP address information; combined with the retrieved information, integrated data strips are generated for each network device, and the data strips corresponding to each device in the local area network are merged and spliced ​​to generate the corresponding bottom-level device information form.

7. The network device management system based on the Internet of Things according to claim 6, characterized in that: The network division module includes a subnet area determination unit and a subnet device information form generation unit; the subnet area determination unit retrieves the corresponding IP address and device type data of each device in the local area network through the underlying device information form; locates the device node in the local area network through the retrieved IP address data of the device; obtains the corresponding subnet mask according to the IP address of the device, and determines the subnet area where the device is located; the subnet device information form generation unit determines the boundary of the subnet area where the device is located, and marks each device in the corresponding boundary, and performs secondary network division in the subnet boundary based on the type data of each device; divides devices with the same type of data into the same networking set; and derives the subnet device information form for each device according to the divided networking set under each subnet boundary.

8. The network device management system based on the Internet of Things according to claim 7, characterized in that: The network equipment comprehensive analysis module includes an equipment virtual self-learning unit, an equipment periodic data flow complexity analysis unit and a dynamic network resource allocation unit; the equipment virtual self-learning unit retrieves the periodic data flow of each device according to the sub-networking equipment information form; the periodic data flow is a data packet sent or received by the device within a set period; if there is a device running time that is less than the period time, a virtual self-learning operation is performed on the device, and the data flow record within the running time of the current device is used as sample data, and the sample data packet data within each time point is randomly selected to form a sample data flow set, and the composed set is summed up to construct a virtual sample data flow set group, and each set data flow in the virtual sample data flow set group is compared with the actual device Calculate the analogy of the data stream set within the running time; take the sample data stream set corresponding to the minimum value of the analogy calculation result as the self-learning data stream set, splice it with the actual device data stream set, and compare the time length of the spliced ​​data stream set with the set period; if the time length of the spliced ​​data stream set is greater than the set period, further analyze the retrieved period data of each device; if the time length of the spliced ​​data stream set is less than the set period, repeat the above virtual self-learning operation until the set period length is met; the device period data stream complexity analysis unit performs data stream complexity detection and analysis on the period data retrieved by each device; continuously calculate the data packets in the period in the order of their corresponding time lines; The system gives the corresponding data packet analogy preset value, compares the calculation result with the preset value, and classifies the data packets at adjacent time points within the result period, including highly overlapping data packets and heterogeneous data packets; performs full calculation of the data packet analogy at adjacent time points of the periodic data of each device, and counts the number of heterogeneous data packets for the calculation results, and calculates the data flow complexity of the periodic data based on the statistical results; The midpoint of the number of devices in the sub-networking device information form is taken as the proposed mapping threshold position, the calculation results of the data flow complexity of the periodic data of each device are sorted from large to small, and the data flow complexity corresponding to the proposed mapping threshold position is taken as the threshold, the calculation result is compared with the threshold, and the device data packet content that is less than the threshold is periodically planned, the data packet is segmented and the repeated fragment content in the high-overlapping data packet is taken as the fixed data packet form specification, and the non-repeated part is taken as the dynamic form specification, otherwise no intervention processing is performed; the dynamic network resource allocation unit performs dynamic network resource management on each network device in combination with the above analysis results; analyzes and calculates by analyzing the network resource usage of each network device within the cycle; the system performs internal comparison according to the bandwidth occupancy conversion rate of each device within the cycle; and dynamically allocates the bandwidth required by each device within the cycle according to the proportion result.

9. The network device management system based on the Internet of Things according to claim 8, characterized in that: The data report feedback module includes a database construction unit and a comprehensive report feedback unit; the database construction unit marks the device nodes in the corresponding local area network according to the sub-networking device information form, and extends through the center point connection line to obtain the associated path point of each device in the space, and uses this point as the database construction point; the comprehensive report feedback unit records the full analysis process and dynamic network resource allocation results of each sub-networking device through the database, and generates a comprehensive report based on the analysis results and allocation results and feeds it back to the local area network management backend.

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